A steadily mounting body of evidence implicates a unique role for sleep in memory processing. Memory traces formed while awake are apparently reactivated during sleep. Cellular recordings during sleep have demonstrated learning-specific, temporally precise replay of neuronal ensembles that were formed during learning. Neuroimaging in humans has documented similar learning-related changes. However, direct, real- time evidence of memory reactivation in humans is currently lacking. The two proposed experiments that are central to the training plan aim to demonstrate memory reactivation in humans by measuring learning-related brain activity with multivariate pattern analysis (MVPA). Participants will learn three different tasks (motor, visuospatial, auditory), each followed by a corresponding rehearsal period, in addition to a baseline control task followed by rest. Classifier algorithms will be trained to discriminate between these conditions using EEG during the actual tasks or EEG during the rehearsal periods. To investigate whether reactivation occurs after learning, the classifier method will be used to assess the level of pattern reinstatement (reactivation strength) during short epochs of sleep before and after learning. A key prediction is that there will be more pattern reinstatement after than before learning, which would implicate memory reactivation during post-learning sleep. Further predictions to be tested are that the degree of memory reactivation, as assessed using the classifier method, will correlate with subsequent memory and will coincide with stages of slow-wave sleep and physiological events (sleep spindles) relevant to memory consolidation. Whereas the goal of the first experiment is to demonstrate a marker of sleep reactivation that relates to subsequent memory, a follow-up experiment will be used to manipulate participants' expectations of future memory tests. Participants will be informed that only one of three memory tests will be given subsequent to the nap. We hypothesize that future memory will be relatively stronger for the expected type of memory in association with an enhancement of learning-related pattern reinstatement. Overall, the proposed studies will provide a focal point for a comprehensive training program and will determine whether EEG measures can be used to classify time intervals when post-learning memory reactivation is engaged. These methods will provide novel perspectives on memory processing during sleep and on memory consolidation in general.